Combining adaptive locomotion primitives

Algorithms that made it possible for a robot to be capable of learning a movement that successfully accomplished the intended goals for a certain instance of a task were an important milestone in robotics and reinforcement learning. However, such capability alone is not enough in order to answer to most challenges when learning motor skills if the learned movement is not expandable to other instances of the task.

Humans appear to be able to learn movement templates, also called movement primitives. That is, they are capable of learning how to execute movement tasks for an entire set of instances at once. For example, it is predictable that after a human learns how to throw a ball in order to hit a target in a certain position, it will take him less time to learn to execute the same task for targets in other positions. This reveals a capability of generalizing the learned knowledge by adapting global parameters of the movement for different instances (with different conditions) of the same task, while keeping the overall shape of the movement.

Combining primitives

After learning adaptive primitives, it is important to be able to combine them in order to achieve complex goals such as adaptive locomotion. For us humans, it is faster to combine acquired knowledge to adapt to a new situation, than to learn everything from the start. We think that the same idea and reasoning can be aplied when learning motor tasks like locomotion in robotics.